Patient Waiting Times in Hospitals - Assignment Example

A LITERATURE REVIEW ON APPLICATIONS OF OPERATIONS RESEARCH IN THE AREA OF HEALTH CARE In this research we will see the application of operations research in the area of health care specifically in the field of "patient waiting times in hospitals". Today there is an increasing public demand for better access, large political pressure for lower wait times for patients in the hospitals, stretching of the workforce along with an aging population and above all the over-utilization of equipment and facilities. This has created am major challenge in front of healthcare leaders to discover newer approaches to health care system and management. In this paper we will see the various benefits that can be attained by applying operations research (OR) methods to the area of healthcare management. We will cover the use of queuing theory for providing managers with insights into the causes for the extreme wait times and finding out the relationship between wait times and the hospital's capacity. We will also cover the use of various OR methods including linear programming and simulation for the optimum scheduling of patients with different priorities or emergency and finally prove that with the help of this approach, proper wait time targets can be achieved along with optimum utilization of available time and resources. 1. INTRODUCTION: Today the health care systems all over the world is facing the problem of long and cumbersome wait times for almost all available medical services (Siciliani and Hurst 2004, and Blendon 2002). Many a times these waits cause a little medical impact; but very often extended delays may cause serious harms to the patient's health (CIHR 2005). Thus there is growing public and patient pressure on the leaders and health care officials to reduce wait times to a reasonable level. The First Ministerial Meeting ever on the Future of Health Care 2004, held at Canada was committed for determining and meeting the wait time benchmarks for cancer patients, cardiac patients and many others like joint replacement and sight/ eye restoration. The creation of these benchmarks will help providing "evidence based goals that express the amount of time that clinical evidence shows is appropriate to wait for a particular procedure or diagnostic test" (Postl 2006). According to his final report created as federal advisor on Wait Times 06, there is a need to make better use of best practices to reduce wait times in which operations research,OR can be most relevant. Operations research is basically used for developing the mathematical models and application of the same to help decision making by providing system models and creating optimal strategies for the management. This helps them to first investigate ant problem and plan the required changes before its implementation. In this paper, we will focus on the use of OR methods for providing insight into the relationship between wait time and capacity. 2. WAITS TO ACCESS HEALTHCARE: The main reason for the wait times in health services are due to the less capacity which does not match the demand for the same or due to its improper management. Here capacity basically means the maximum rate at which any resource can deliver a particular service when operating at its peak efficiency (Anupindi et.al, 2005). Capacity is managed by investing and scheduling of the optimum use of plant and machinery and people. But there exists an unavoidable trade-off between the resource utilization and the wait times. For example: If capacity increases over the average demand, there will be shorter queues and the least wait times. But due to the variable demand, attaining this level of control would need the resources to remain idle for a large portion of time. This is shown in the figure. And when the capacity is very low than the average demand, the hospital resources can be utilized fully but the wait times will be much more and growing over time. Another case is that when capacity is equivalent or just exceeds the average demand, than according to the queuing theory (Hillier and Lieberman 2001) long waits are inevitable. Here there will be full utilization of the resources most of the time. Well the decisions regarding investment in the area of capacity should fully account for the tradeoff given in the figure. This relationship is derived from the fundamental queuing theory (Hillier and Lieberman, 2001, p.854-855). According to this, to ensure that a only a minimum percentage of patients exceed wait time targets, the capacity must be planned so high that some idle time is always remaining. The dotted line in the graph explains that to ensure that only 5% of patients exceed the wait time, there should be an idle capacity i.e. 23% of the time.(Jonathan Patrick, Martin L. Puterman, 2007). Source: http://www.chcm.ubc.ca/documents/HealthPolicyPaper-Feb15.pdf 3. APPLICATION OF OPERATIONS RESEARCH IN THE AREA: 3.1 CRITICAL ISSUES: There are some complex issues in the capacity management which affect both the setting of and achieving of wait time target such as: Both the patients and their priorities are not homogeneous. There are some patients who may require urgent care and they should receive the services before those requiring less-urgent care. Thus the wait times should be assessed against the proper benchmarks for every priority class. The whole story cannot be judged by the wait times only. Usually wait times are measured when any request is asked for, from the service provider. They do not take any responsibility for any delays between the time when a service is first needed and when the physicians can see the patient and put them into the proper queue. Wait times always vary between patients, over time patients. This variability should be considered as a part of the performance measurement system. So taking average only does not always help. Further, the distribution of wait times is skewed many times. Here, the metrics form can be made use of which will help by providing guarantees to the management and the system users. 3.2 APPLICATION OF THE OR METHODS: The methods of operations research can help the hospital and health managers' better plan and manage the available capacity to meet wait time targets. This is done in many ways which are as following: 1. Capacity planning: capacity planning is important to know that how much capacity is needed to meet the present and future wait time targets. Here we can make use of the Queuing system and simulation models. This will help to understand the variability for the determination of proper capacity levels required. The System wide planning models which are based upon the linear and integer programming (Atkins et al, 2006 ) can be made use of to decide whether and when to add system capacity. 2. Capacity management: it is necessary to properly assign the total demand to the capacity. This would allow the efficient use of capacity and ensure efficacy as per the situation. The methods to for the effective management of capacity include: Better scheduling of patients, better scheduling of staff and re-modeling of the whole system for the better management of all resources that a patient would need while scheduling all the services. This can be made easier by making use of Simulation models, linear programming and Markov decision process. 3. Matching capacity with the demand: If the average demand and average capacity level, the system would work efficiently and there will be no variations. Otherwise, the variability may lead to long and excessive wait times. Source: http://healthcareleadershipconference.ca/assets/PDFs/Presentation%20PDFs/June%2012/Dockside%203/Reducing%20Wait%20Times%20through%20Intelligent%20Patient%20Scheduling.pdf 4. Surge capacity: it is a different approach to planning and capacity management. Surge Capacity basically means the extra capacity for example: overtime, which can be put to use when needed to meet excessive demand. As explained above, when the capacity to manage and achieve the wait times is set high to the required level, the capacity will be just an idle fraction of the time. However, when the capacity is set for its full utilization, it may so happen that a large number of patients may not achieve their required wait time targets. A possible solution to this trade-off is to have the surge capacity to allow the system for its proper functioning with less permanent capacity leading to less unused capacity while still meeting the demand. The management of surge capacity requires two things, first, determination of proper base capacity and second, development of certain rules that specify when and how much surge capacity is to be used. According to Postl, the need for research in this area is to explore the use and advantages of the surge capacity (Postl 2006). 4. INTELLIGENT SCHEDULING: In most of the hospitals, the scheduling of patients is done by schedulers who are able to make difficult tradeoffs when the intelligent software and decision rules to support their decisions. This activity is very challenging and becomes very complex in various cases like When the patients are put into various priority classes with differences in service time targets When there are various types of equipments having different capabilities by which a patient can be properly scheduled When the patients are to be booked for a particular treatment which may require several days or weeks Or even when all the resources are spread over a wide geographic region Take for example: Everyday, there is a random number of appointment requests which are reviewed by the scheduler. Then the appointments are assigned to them and the future appointment slots are also managed. Every request is different and has a priority assigned to it. Different priorities have different maximum and minimum wait times attached to it. The main task faced by the scheduler is that the lower priority cases must be booked at the present day for an appointment given at some future day and time, prior to even knowing about future demand. However if the booking of patients is done for a far future date, it may so happen that they may not meet the priority targets, resulting in idle staff and equipments. And on the other hand, booking too soon, results in insufficient capacity to meet wait time targets, and the high priority patients arriving at a later date may suffer. Thus some precise decision rules should be followed, which help the schedulers to make efficient decisions and meet the priority targets which are known as intelligent scheduling (IS). According to Gerchak et al (1996),Green et al. (2006), and Gupta and Wang (2003), the main objective in private health care setting is to maximize revenue instead of achieving proper wait time targets. But till date, there is no research done on multi-class scheduling in the healthcare system, where the wait time targets can be explicitly stated and achieved. In this case, the Markov decision process or MDP model of the scheduling process can be put to use. It is a system in whereby the sequential decisions are made over time. Here, the future decisions and its outcomes are totally dependent on past and present decisions (Puterman 1994). An MDP model provides us with optimal policy that guides us how to manage the system under any emergency or contingency to its best. It undermines the old approach of guessing and checking by using the simulation models to determine good policies on its own. In this case the policy would provide a scheduler with a set of rules stating where and when in the future to schedule each and every patient waiting. 5. CONCLUSION: The scheduler ought to schedule the remaining high priority patients to the earliest available time slots and not waiting for the maximum recommended time If in case there is extreme high priority demands which cannot be scheduled before its maximum recommended wait time, they should serve it by doing overtime The Intelligent Scheduling Policy should be made proper use of by filling the unused capacity if any, for the coming day, by assigning it to the patients on that day in priority order They should also schedule all other classes for the last available day. This will not increase the maximum recommended waiting time for that particular class. In case of multi-priority patient scheduling, the following can be done: If the surge capacity can't be made use of, there will be either many wait times or much idle capacity so, only with very little overtime, the wait times can be maintained and managed within the targets without more requirement of capacity. There should be flexibility in the scheduling of highest priority class scheduling which will help the most to the resource manager to maintain and manage reasonable wait times for minimal cost. Thus the combination of some policies with the use of various OR methods like queuing theory, linear programming, simulation models , MDP etc will prove useful for the better management and scheduling of patient waiting times in hospitals and to ensure better access to health care. REFERENCES: 1. Atkins D.A, Begen M and Santibanez P, Managing Surgical Waitlists for A British Columbia Health Authority, UBC Centre for Health Care Management Working Paper, 2006. 2. Blendon R, Inequities in health care: A five country survey, Health Affairs 21,2002 pg182-191. 3. Canadian Institute of Health Research, CIHR Release, Research Results to Inform the Development of Benchmarks for Wait Times ,Ottawa, 2005, modified Nov 16th 05,http://www.cihr-irsc.gc.ca/e/29904.html. 4. Patrick Jonathan, Puterman Martin L, Reducing Wait Times Through Operations Research: The Case for Intelligent Patient Scheduling, Sauder School of Business, UBC,Feb15, 2007. http://www.chcm.ubc.ca/documents/HealthPolicyPaper-Feb15.pdf. 5. Hurst, J. and. Siciliani L, Tackling excessive waiting times for elective surgery: A comparison of policies in twelve OECD countries. OECD Health Working Papers 6, 2006. 6. Gerchak, Y.,Gupta D and Henig M, Reservation planning for elective surgery under uncertain demand for emergency surgery. Management Science, 1996, pg 321-334. 7. Green, L., Savin S and Wang B,Managing patient demand in a diagnostic medical facility, Operations Research 2006. pg11-25. 8. Gupta, D. and Wang L Revenue management for a primary-care clinic in presence of patient choice,Under review with Operations Research, 2005 9. Hillier and Lieberman, 2001, pg854-855. 10. Postl B, The final report of the federal advisor on wait times, 2006. 11. Puterman M, Markov Decision Processes. New York, NY: John Wiley and Sons, 1994. Read More